Existing algorithms for maritime threat detection employ a variety of normalcy models that are probabilistic and/or rule-based. Unfortunately, they can be limited in their ability to model the subtlety and complexity of multiple vessel types and their spatio-temporal events, yet their representation is needed to accurately detect anomalies in maritime scenarios. To address these limitations, we apply plan recognition algorithms for maritime anomaly detection. In particular, we examine hierarchical task network (HTN) and case-based algorithms for plan recognition, which detect anomalies by generating expected behaviors for use as a basis for threat detection. We compare their performance with a behavior recognition algorithm on simulated riverine maritime traffic. On a set of simulated maritime scenarios, these plan recognition algorithms outperformed the behavior recognition algorithm, except for one reactive behavior task in which the inverse occurred. Furthermore, our case-based plan recognizer outperformed our
HTN algorithm. On the short-term reactive planning scenarios, the plan recognition algorithms outperformed the behavior recognition algorithm on routine plan following. However, they are significantly outperformed on the anomalous scenarios.